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Can the promising FDO concept be realised using existing Web technology, taking into account the lessons learnt from the early Semantic Web developments and more recent Linked Data practices?
Can a more pragmatic use of Linked Data practices better implement Research Objects for a wider developer audience, by using familiar Web technologies and give lightweight recommendations?
Can a FAIR Digital Object approach for computational workflows unify machine-readable descriptions of Research Software, data and provenance, which can be consistently implemented by developers of different workflow management systems?
Section 5.2: The Specimen Data Refinery: A canonical workflow framework and FAIR Digital Object approach to speeding up digital mobilisation of natural history collections
Section 5.2: The Specimen Data Refinery: A canonical workflow framework and FAIR Digital Object approach to speeding up digital mobilisation of natural history collections
Alex Hardisty, et al. (2022): The Specimen Data Refinery: A canonical workflow framework and FAIR Digital Object approach to speeding up digital mobilisation of natural history collections. Data Intelligence 4(2)
In this chapter, we discuss the related work with respect to FAIR Digital Objects and Linked Data. We do so by looking through the lens of development of these technologies over time, including future directions.
To investigate RQ1 this chapter evaluates both Linked Data and FAIR Digital Object (FDO) as ways to realize the FAIR principles. Section 3.1 compares the two approaches as global distributed object systems, and discusses what lessons can be learnt across the communities, taking into consideration the history covered by section 2. Section 3.2 proposes how the FDO principles can be achieved using Linked Data standards, which is explored further in the following chapters.
This chapter introduces RO-Crate, a pragmatic method of packaging data alongside structured metadata that is inline with the FAIR principles. This has been implemented to investigate RQ2. Section 4.1 describes the RO-Crate purpose, community effort and tooling and demonstrates how RO-Crate has been applied. Section 4.2 shows how RO-Crate can be used to achieve the FDO principles covered in chapter 3. Section 4.3 contributes a formal definition of RO-Crate using first order logic.
In order to investigate RQ3, and considering important parts of the FAIR principles are Reuse and provenance, this chapter examines in closer details how FAIR Digital Objects and RO-Crate can be used with Computational Workflows. Section 5.1 proposes that tools in computational workflows, when wrapped as interoperable building blocks, can be considered as FAIR Digital Objects, with a use case from biomolecular simulation. Sections 5.2 and 5.3 explore how FDOs and Research Objects can be constructed incrementally using computational workflows, with a use case from specimen digitization in natural history collections. Section 5.4 presents a profile of RO-Crate to capture workflow execution provenance, with incremental granularity levels and six workflow engine implementations. Use cases include machine learning-aided tumour detection and compatibility with PROV approaches.
